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The Potential and Limitations of Large Language Models for Automatic Classification of Teachers' Motivational Messages in Educational Research

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Title: The Potential and Limitations of Large Language Models for Automatic Classification of Teachers' Motivational Messages in Educational Research
Language: English
Authors: Olivia Metzner (ORCID 0009-0006-0953-5839), Yindong Wang (ORCID 0009-0001-3946-1432), Gerard Melo (ORCID 0000-0002-2930-2059), Wendy Symes (ORCID 0000-0003-2110-0505), Yizhen Huang (ORCID 0000-0002-7041-1927), Rebecca Lazarides (ORCID 0000-0003-0392-4981)
Source: British Journal of Educational Psychology. 2026 96(1):14-31.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 18
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Artificial Intelligence, Natural Language Processing, Classification, Teacher Motivation, Educational Research, Motivation Techniques
DOI: 10.1111/bjep.70013
ISSN: 0007-0998
2044-8279
Abstract: Introduction: The rapid advancement of artificial intelligence (AI) has created new opportunities in educational research, particularly in the efficient analysis of complex social interactions within classrooms. One promising area involves the classification of teachers' motivational messages. Traditionally, such assessments have relied on self-reports and observer evaluations, which require a lot of staff and time resources. Recently, large language models (LLMs) have been employed to classify teachers' motivational messages, offering novel, less labour-intensive approaches for classification. Aims: Building on these recent developments, this work presents a comprehensive literature overview exploring the applications, potential, and limitations of using LLMs to classify teachers' motivational messages. Results: The present comprehensive literature overview indicates that the use of LLMs for classifying teachers' motivational messages is a promising yet still emerging field of research. Recent studies have applied LLMs in innovative ways, drawing on established motivational theories and employing novel classification techniques, such as zero-shot and few-shot prompting or fine-tuning, to classify motivational messages. Open questions remain, particularly concerning the structure, quantity, and quality of annotated material. Discussion: Whereas recent studies have demonstrated the potential of LLMs to offer scalable and time-efficient alternatives for classifying motivational messages in the classroom, several challenges persist. These include concerns related to the quality and quantity of training data, model generalisability, the ability to capture the complexity of classroom interactions, and biases involved in integrating LLMs as a classification method. This comprehensive literature overview provides practical recommendations for the responsible use of LLMs in educational research and school practice.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1496204
Database: ERIC
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  Data: The Potential and Limitations of Large Language Models for Automatic Classification of Teachers' Motivational Messages in Educational Research
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  Data: <searchLink fieldCode="AR" term="%22Olivia+Metzner%22">Olivia Metzner</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-0953-5839">0009-0006-0953-5839</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yindong+Wang%22">Yindong Wang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-3946-1432">0009-0001-3946-1432</externalLink>)<br /><searchLink fieldCode="AR" term="%22Gerard+Melo%22">Gerard Melo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2930-2059">0000-0002-2930-2059</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wendy+Symes%22">Wendy Symes</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2110-0505">0000-0003-2110-0505</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yizhen+Huang%22">Yizhen Huang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7041-1927">0000-0002-7041-1927</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rebecca+Lazarides%22">Rebecca Lazarides</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0392-4981">0000-0003-0392-4981</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22British+Journal+of+Educational+Psychology%22"><i>British Journal of Educational Psychology</i></searchLink>. 2026 96(1):14-31.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 18
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Motivation%22">Teacher Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation+Techniques%22">Motivation Techniques</searchLink>
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  Data: 10.1111/bjep.70013
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  Data: Introduction: The rapid advancement of artificial intelligence (AI) has created new opportunities in educational research, particularly in the efficient analysis of complex social interactions within classrooms. One promising area involves the classification of teachers' motivational messages. Traditionally, such assessments have relied on self-reports and observer evaluations, which require a lot of staff and time resources. Recently, large language models (LLMs) have been employed to classify teachers' motivational messages, offering novel, less labour-intensive approaches for classification. Aims: Building on these recent developments, this work presents a comprehensive literature overview exploring the applications, potential, and limitations of using LLMs to classify teachers' motivational messages. Results: The present comprehensive literature overview indicates that the use of LLMs for classifying teachers' motivational messages is a promising yet still emerging field of research. Recent studies have applied LLMs in innovative ways, drawing on established motivational theories and employing novel classification techniques, such as zero-shot and few-shot prompting or fine-tuning, to classify motivational messages. Open questions remain, particularly concerning the structure, quantity, and quality of annotated material. Discussion: Whereas recent studies have demonstrated the potential of LLMs to offer scalable and time-efficient alternatives for classifying motivational messages in the classroom, several challenges persist. These include concerns related to the quality and quantity of training data, model generalisability, the ability to capture the complexity of classroom interactions, and biases involved in integrating LLMs as a classification method. This comprehensive literature overview provides practical recommendations for the responsible use of LLMs in educational research and school practice.
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